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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cubillos, Luis H. Revach, Guy Mender, Matthew J. Costello, Joseph T. Temmar, Hisham Hite, Aren Zutshi, Diksha Wallace, Dylan M. Ni, Xiaoyong Kelberman, Madison M. Willsey, Matthew S. van Sloun, Ruud J.G. Shlezinger, Nir Patil, Parag Draelos, Anne Chestek, Cynthia A. Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan United States Biointerfaces Institute Neuroscience Institute University of Michigan United States Department of Information Technology and Electrical Engineering ETH Zürich Switzerland Department of Electrical Engineering Eindhoven University of Technology Netherlands School of Electrical and Computer Engineering Ben-Gurion University Israel

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n = 13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite thi

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Tracking Blood Glucose Concentration of Type 1 Diabetic Patients Using Reinforcement Learning 9

Tracking Blood Glucose Concentration of Type 1 Diabetic Pati...

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9th International Conference on Control, Instrumentation and Automation, ICCIA 2023

作者： Sabzevar, Peyman Vafadoost Sadrian, Hamid Hajipour, Ahmad Hakim Sabzevari University Electrical and Computer Faculty Biomedical Engineering Department Sabzevar Iran

ISBN: (纸本)9798350366013

Blood glucose control in diabetic patients has become one of the main concerns of researchers due to its widespread growth in society in recent years. Additionally, during the COVID-19 pandemic, research indicates that individuals who have diabetes have a higher susceptibility to contracting COVID-19 and experiencing more severe complications. This is attributed to a compromised immune system, resulting in a greater number of fatalities compared to individuals without diabetes. Therefore, the desired controller for blood glucose regulation should be very stable against hypoglycemia and perturbations such as meals, noise, and uncertainty, such as the pancreas of a healthy person. The metabolic disorders that cause diabetes in people are covered first in this article. Then Reinforcement Learning will be introduced as one of the machine learning methods to control the blood glucose concentration, and its robustness in the presence of disturbance will be investigated. © 2023 IEEE.

关键词： Reinforcement learning

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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Multi-Vision-Based Picking Point Localisation of Target Fruit for Harvesting Robots

Multi-Vision-Based Picking Point Localisation of Target Frui...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Beldek, C. Dunn, A. Cunningham, J. Sariyildiz, E. Phung, S.L. Alici, G. School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong WollongongNSW Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia

ISBN: (纸本)9798331533892

This paper presents multi-vision-based localisation strategies for harvesting robots. Identifying picking points accurately is essential for robotic harvesting because insecure grasping can lead to economic loss through fruit damage and dropping. In this study, two multi-vision-based localisation methods, namely the analytical approach and model-based algorithms, were employed. The actual geometric centre points of fruits were collected using a motion capture system (mocap), and two different surface points Cfix and Ceih were extracted using two Red-Green-Blue-Depth (RGB-D) cameras. First, the picking points of the target fruit were detected using analytical methods. Second, various primary and ensemble learning methods were employed to predict the geometric centre of target fruits by taking surface points as input. Adaboost regression, the most successful model-based localisation algorithm, achieved 88.8% harvesting accuracy with a Mean Euclidean Distance (MED) of 4.40 mm, while the analytical approach reached 81.4% picking success with a MED of 14.25 mm, both demonstrating better performance than the single-camera, which had a picking success rate of 77.7% with a MED of 24.02 mm. To evaluate the effect of picking point accuracy in collecting fruits, a series of robotic harvesting experiments were performed utilising a collaborative robot (cobot). It is shown that multi-vision systems can improve picking point localisation, resulting in higher success rates of picking in robotic harvesting. © 2025 IEEE.

关键词： Adversarial machine learning

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Evaluation of Commercially Available Fall Detection Systems

Evaluation of Commercially Available Fall Detection Systems

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17th International Conference of the Association for the Advancement of Assistive Technology in Europe, AAATE 2023

作者： Hrubý, Dan Černý, Martin Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science Vsb - Technical University of Ostrava Czech Republic

ISBN: (纸本)9781643684222

Falls are a serious problem in the hospital setting and home environments. However, this problem does not only affect the elderly, but also people who have had surgery, have disabling problems, have associated diagnoses (such as poor eyesight, confusion, etc.) or are dizzy or have walking aids. The aim of research was to find, compare and implement fall detectors especially for the hospital environment. This paper summarizes possible fall detectors. Various technological solutions were selected for testing, including wearable technologies as well as contactless technologies based on PIR detectors and mmWave technologies. The selected fall detectors were tested in living laboratory of *** and then in Hospital AGEL Třinec - Podlesí. The best result of the testing was the use of two Vayyar Home Care devices in one room, thus achieving a detection accuracy of 92.50 % and a sensitivity of 92.50 %. © 2023 IOS Press. All rights reserved.

关键词： Millimeter waves

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Field Uniformity Optimization for Integrated Capacitance Sensing of Tumor Treating Field-Treated Cancer Cell Cultures 66

Field Uniformity Optimization for Integrated Capacitance Sen...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Gilpin, Yann Yankel, Joseph Dandin, Marc Department of Electrical and Computer Engineering United States Carnegie Mellon University Department of Biomedical Engineering Pittsburgh United States

ISBN: (纸本)9798350302103

An emerging modality in cancer treatment is tumor treating fields (TTF). It consists of electric fields of approximately 1 VRMS/cm in strength and of around 200 kHz in frequency. When these fields are aligned with a cancer cell's mitotic spindle, they can impede cell division. This method has improved overall patient survival in glioblastoma patients, remarkably with limited side effects. However there are still many unknowns associated with this therapy. Thus, to assist the discovery effort and facilitate further studies, we previously designed and successfully tested an in vitro integrated capacitance sensing TTF microsystem for autonomous and label-free detection of the effect TTFs on breast cancer cells. Herein, we propose an update to the microsystem's electrode geometry. Our novel approach eases the trade-offs between size, material usage, and consistency of treatment. Based on simulation studies and a physical model, we show that the new approach reduced an applied field's magnitude variance by 63% and angle variance by 54%. These gains should translate to improved repeatability, looser manufacturing tolerances, and smaller designs. © 2023 IEEE.

关键词： cancer cells CMOS electric field field optimization integrated capacitance sensing Tumor-treating fields

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Balancing Memorization and Generalization in RNNs for High Performance Brain-Machine Interfaces 37

Balancing Memorization and Generalization in RNNs for High P...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Costello, Joseph T. Temmar, Hisham Cubillos, Luis H. Mender, Matthew J. Wallace, Dylan M. Willsey, Matthew S. Patil, Parag G. Chestek, Cynthia A. Department of Electrical and Computer Engineering Biomedical Engineering Robotics and Neurosurgery University of Michigan Ann ArborMI United States

ISBN: (纸本)9781713899921

Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Mammals

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Visual Saliency Approaches for Defect Detection in Industrial Processes: A Short Review 29

Visual Saliency Approaches for Defect Detection in Industria...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Porta, Marco University of Pavia Dept. of Electrical Computer and Biomedical Engineering Via A. Ferrata 5 Pavia27100 Italy

ISBN: (纸本)9798350361230

Finding defects during production is crucial in industrial manufacturing. While various detection methods exist, this paper proposes a short review on techniques based on visual saliency. Saliency approaches try to mimic how humans see and are characterized by advantages such as accuracy, speed, and ability to handle complex situations. With solutions based on deep learning becoming increasingly dominant, this review aims to provide a brief analysis of the current state of research in this field, possibly stimulating continued exploration and development of new techniques. © 2024 IEEE.

关键词： computer vision defect detection industrial production visual saliency

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Alzheimer’s Disease and Frontotemporal Dementia: Differential Diagnosis Using Electroencephalogram Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Rostamikia, Mehran Sarbaz, Yashar Farzamnia, Ali Biomedical System Modeling Lab Biomedical Engineering Department Electrical and Computer Engineering Faculty University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Dementia is a neurological disorder that affects a person’s cognitive and social skills, leading to a decline in their overall mental functioning. Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) are two common types of dementia. Frontotemporal dementia mostly affects the frontal and temporal lobes of the brain. These areas are responsible for executive functions, decision-making, language, behavior regulation, and personality traits. Alzheimer’s disease primarily damages the cerebral cortex, which is responsible for higher cognitive functions such as memory, language, and perception. The electroencephalogram (EEG) signal has many advantages in diagnosing these disorders, including low cost and high temporal resolution. This study compares AD and FTD patients with healthy subjects by extracting specific features from EEG signals. Two machine learning algorithms were used for the separation, Support Vector Machines (SVMs) and k-Nearest Neighbors (KNNs), and 10-Fold Cross-Validation was applied to validate the performance of this method and an accuracy of 91.2% (sd = 7.8) was achieved using the SVM classifier for diagnosing the disease and 71.1% (sd = 8.6) for classifying AD and FTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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